The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Object detection, such as face detection, pedestrian detection, and/or car detection, has broad applications in various fields, such as surveillance, electronic commerce, advertisement, and/or autonomous driving. For example, in electronic commerce and/or advertisement, object detection may be used to identify related objects in images, such as photographs and/or video. Upon identifying related objects, a relevant recommendation and/or a targeted advertisement may be generated and/or rendered to a user.
Object detection methods may be computationally expensive, and the resulting latency in processing time may be impractical for certain computer systems, such as mobile devices. For example, a sliding-window-based method applies an object detection filter at every possible position and scale of an image in a sliding-window manner. Assuming x and y are the dimensions of the image; m and n are the dimensions of the binary classifier; k is the number of binary classifiers; and d is the number of distinct gradient directions, the algorithmic complexity of the sliding-window-based method is O(kdxymn).
While each of the drawing figures depicts a particular embodiment for purposes of depicting a clear example, other embodiments may omit, add to, reorder, and/or modify any of the elements shown in the drawing figures. For purposes of depicting clear examples, one or more figures may be described with reference to one or more other figures, but using the particular arrangement depicted in the one or more other figures is not required in other embodiments.